Methods and systems for detecting an environmental zone in a region

ABSTRACT

System, method and computer program products are provided for detecting an environmental zone, generating a time schedule of an environmental zone in a region and providing route navigation instructions. The method may include obtaining at least one observation associated with the environmental zone in a region. The method may further include determining a confidence value associated with the at least one observation in the region and detecting the environmental zone in the region based on the confidence value associated with the at least one observation. The detection of the environmental zone comprises determining either one of a presence or an absence of the environmental zone in the region. Further, the detection of the environmental zone may be used to generate a time schedule indicating presence or absence of the environmental zone in the region for different time intervals.

TECHNOLOGICAL FIELD

The present disclosure generally relates to routing and navigationapplications, and more particularly relates to systems and methods fordetecting an environmental zone in a region for routing and navigationapplications.

BACKGROUND

Various navigation applications are available to aid, for example,directions for driving, walking, or other modes of travel. Web-based andmobile app-based systems offer navigation applications that allow a userto request directions from one point to another. Often, a routetraversed or to be traversed by a user encompasses several roadsincluding environmental zones that are restricted for vehicles and othertypes of users.

Some environmental zones correspond to green zones that haverestrictions on vehicle movement and/or operations. For example,vehicular movement into and out of the green zone is restricted, avehicle (and thus a corresponding user) may have to pay a fee forentering the green zone, and the like. Thus, it would be helpful if theuser is aware of the green zones and can be provided with reliableinformation in this regard.

BRIEF SUMMARY

Green zones may be special type of environmental zones which aredemarcated by pollution levels in a region in some situations. Forexample, when the pollution level in an area is more than a thresholdvalue then all the vehicles may be restricted to enter that area for aspecified time period. Similarly, sometimes the vehicle causingpollution level more than the threshold value may be restricted to enterthe environmental zone. Sometimes, only the pedestrians, cyclists, andvehicles with green stickers may be allowed to enter the environmentalzone to keep the pollution level in the green zone under control. Forthe purpose of explanation within the description in the followingpages, the terms “green zone” and “environmental zone” may be usedinterchangeably to mean the same. However, the use of these terms in themanner suggested herein is not intended to limit the scope of thisdescription and the term “environmental zone” in any way, as may beunderstood by a person of ordinary skill in the art. Therefore, there isa need for systems and methods that can determine the coverage of theenvironmental zone in a reliable, updated and efficient manner and alsohave an ability to generate a time schedule of the environmental zone,which may be used to provide navigational instructions to the vehiclesbased on the coverage and time schedule of the detected environmentalzone.

Accordingly, in order to provide accurate and reliable navigationassistance, it is important to detect an environmental zone and generatea time schedule of an environmental zone in a region. To this end, thedata utilized for providing navigation assistance should provideaccuracy in generating time schedule of the environmental zone in theregion. Especially, in the context of navigation assistance forautonomous vehicles and semi-autonomous vehicles, to avoid inaccuratenavigation, it is important that the assistance provided is real-timeand accurate. More importantly, in the context of autonomous vehicles,it is of utmost importance that the navigation assistance shouldgenerate a time schedule of the environmental zone and provide analternative route to traverse to the autonomous vehicle well in time, incase navigation restrictions due to existence of environmental zoneconditions are anticipated. Example embodiments of the presentdisclosure provide a system, a method, and a computer program productfor detecting an environmental zone in a region and generating a timeschedule of an environmental zone in the region.

Some example embodiments disclosed herein provide a method for detectingan environmental zone, the method comprising obtaining at least oneobservation associated with the environmental zone in a region. Themethod may further include determining a confidence value associatedwith the at least one observation in the region and detecting theenvironmental zone in the region based on the confidence valueassociated with the at least one observation in the region, whereindetecting comprises determining either one of a presence or an absenceof the environmental zone in the region.

According to some example embodiments, detecting the environmental zonefurther comprises detecting a coverage area associated with theenvironmental zone in the region.

According to some example embodiments, the method further comprisesgenerating a time schedule for the environmental zone based on thedetermined coverage area of the environmental zone in the region.

According to some example embodiments, the method further comprisespredicting either one of the presence or absence of the environmentalzone based on the generated time schedule and the coverage area of theenvironmental zone.

According to some example embodiments, obtaining the at least oneobservation associated with the environmental zone in the region furthercomprises obtaining the at least one observation based on at least oneof road sign data, one or more pollution sensors, and one or more othersensors in a vehicle.

According to some example embodiments, the at least one observationfurther comprises at least one of a positive observation and a negativeobservation. The positive observation is associated with a firstdetermination of presence of environmental zone in the region. Thenegative observation is associated with a second determination ofabsence of environmental zone in the region.

According to some example embodiments, each of the at least one positiveobservation and the at least one negative observation is associated witha time interval associated with each day in a week.

According to some example embodiments, determining the confidence valueassociated with the at least one observation further comprisesdetermining a plurality of observations in the region for the timeinterval associated with the at least one observation, wherein theplurality of observations include a plurality of positive observationsand a plurality of negative observations. The plurality of positiveobservations may be aggregated to determine an aggregated positiveobservation value. The plurality of negative observations may beaggregated to determine an aggregated negative observation value. Theconfidence value may then be determined based on the aggregated positiveobservation value and the aggregated negative observation value.

According to some example embodiments, determining the confidence valuefurther comprises monitoring, in real time, a change in confidence valueassociated with the at least one observation in the region.

According to some example embodiments, the method further comprisesdetermining confidence value of at least one missing observation for theregion based on a historical confidence value associated with the atleast one observation.

According to some example embodiments, the method further comprisesgenerating navigational alerts associated with the detection of theenvironmental zone in the region.

According to some example embodiments, the method further comprisesgenerating alternate routes for navigation based on the detection of theenvironmental zone.

According to some example embodiments, the method further comprisesupdating a coverage of the environmental zone in a map database, whereinthe coverage is indicated by a polygon shape in the map database.

According to some example embodiments, the region comprises at least oneof a location point, a map tile area, a road segment, and a lane.

Some example embodiments disclosed herein provide a system forgenerating a time schedule of an environmental zone in a region, thesystem comprising a memory configured to store computer-executableinstructions and one or more processors configured to execute theinstructions to obtain, for a predefined time interval, at least oneobservation associated with the environmental zone in the region. Theone or more processors are further configured to execute theinstructions to determine, for the predefined time interval, aconfidence value associated with the at least one observation andgenerate a time schedule for the environmental zone in the region basedon the determined confidence value and the predefined time interval.

Some example embodiments disclosed herein provide a computerprogrammable product comprising a non-transitory computer readablemedium having stored thereon computer executable instructions which whenexecuted by one or more processors, cause the one or more processors tocarry out operations for providing navigation instructions, theoperations comprising obtaining route information for navigation of atleast one vehicle in a region. The operations further comprisedetermining, based on map data and the route information, at least onelocation associated with a confidence value related to an environmentalzone in the region. The operations further comprise determining acoverage area for the environmental zone in the region based on thedetermined at least one location and providing the navigationinstructions for operation of the at least one vehicle in the regionbased on the determined coverage area for the environmental zone in theregion.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described example embodiments of the invention in generalterms, reference will now be made to the accompanying drawings, whichare not necessarily drawn to scale, and wherein:

FIG. 1 illustrates a schematic diagram of a network environment of asystem for detecting an environmental zone and generating a timeschedule of an environmental zone in a region, in accordance with anexample embodiment;

FIG. 2 illustrates a block diagram of a system for detecting anenvironmental zone and generating a time schedule of an environmentalzone in a region, in accordance with an example embodiment;

FIG. 3A illustrates an exemplary scenario for detecting an environmentalzone and generating a time schedule of an environmental zone in aregion, in accordance with an example embodiment;

FIG. 3B illustrates an exemplary scenario for obtaining observationsrelated to an environmental zone in a region, in accordance with anexample embodiment;

FIGS. 4A-4B illustrate exemplary tables for obtaining observationsrelated to an environmental zone in a region, in accordance with one ormore example embodiments;

FIG. 4C illustrates an exemplary method for updating the tables shown inFIGS. 4A-4B, in accordance with an example embodiment;

FIG. 4D illustrates an exemplary scenario for confidence valuedetermination, in accordance with an example embodiment;

FIGS. 5A-5B illustrate an exemplary representation of map data fordisplaying an environmental zone in a region, in accordance with one ormore example embodiments; and

FIGS. 6A-6C illustrate flow diagrams of methods for detecting anenvironmental zone and generating a time schedule of an environmentalzone in a region, in accordance with one or more example embodiments.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,to one skilled in the art that the present disclosure can be practicedwithout these specific details. In other instances, systems, apparatusesand methods are shown in block diagram form only in order to avoidobscuring the present disclosure.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the present disclosure. The appearance of the phrase “in oneembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Further, the terms“a” and “an” herein do not denote a limitation of quantity, but ratherdenote the presence of at least one of the referenced items. Moreover,various features are described which may be exhibited by someembodiments and not by others. Similarly, various requirements aredescribed which may be requirements for some embodiments but not forother embodiments.

Some embodiments of the present invention will now be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all, embodiments of the invention are shown. Indeed,various embodiments of the invention may be embodied in many differentforms and should not be construed as limited to the embodiments setforth herein; rather, these embodiments are provided so that thisdisclosure will satisfy applicable legal requirements. Like referencenumerals refer to like elements throughout. As used herein, the terms“data,” “content,” “information,” and similar terms may be usedinterchangeably to refer to data capable of being transmitted, receivedand/or stored in accordance with embodiments of the present invention.Thus, use of any such terms should not be taken to limit the spirit andscope of embodiments of the present invention.

Additionally, as used herein, the term ‘circuitry’ may refer to (a)hardware-only circuit implementations (for example, implementations inanalog circuitry and/or digital circuitry); (b) combinations of circuitsand computer program product(s) comprising software and/or firmwareinstructions stored on one or more computer readable memories that worktogether to cause an apparatus to perform one or more functionsdescribed herein; and (c) circuits, such as, for example, amicroprocessor(s) or a portion of a microprocessor(s), that requiresoftware or firmware for operation even if the software or firmware isnot physically present. This definition of ‘circuitry’ applies to alluses of this term herein, including in any claims. As a further example,as used herein, the term ‘circuitry’ also includes an implementationcomprising one or more processors and/or portion(s) thereof andaccompanying software and/or firmware. As another example, the term‘circuitry’ as used herein also includes, for example, a basebandintegrated circuit or applications processor integrated circuit for amobile phone or a similar integrated circuit in a server, a cellularnetwork device, other network device, and/or other computing device.

As defined herein, a “computer-readable storage medium,” which refers toa non-transitory physical storage medium (for example, volatile ornon-volatile memory device), can be differentiated from a“computer-readable transmission medium,” which refers to anelectromagnetic signal.

The embodiments are described herein for illustrative purposes and aresubject to many variations. It is understood that various omissions andsubstitutions of equivalents are contemplated as circumstances maysuggest or render expedient but are intended to cover the application orimplementation without departing from the spirit or the scope of thepresent disclosure. Further, it is to be understood that the phraseologyand terminology employed herein are for the purpose of the descriptionand should not be regarded as limiting. Any heading utilized within thisdescription is for convenience only and has no legal or limiting effect.

Definitions

The term “link” may be used to refer to any connecting pathway includingbut not limited to a roadway, a highway, a freeway, an expressway, alane, a street path, a road, an alley, a controlled access roadway, afree access roadway and the like.

The term “route” may be used to refer to a path from a source locationto a destination location on any link.

The term “environmental zone” may refer to a routing zone such as anarea or a road or a link or a pathway on which restrictions may beimposed on movement and/or operations of vehicles based on factors suchas emission levels of vehicles, environmental pollution levels, type ofvehicle and vehicle characteristics and the like. The environmental zonemay interchangeably be referred to as a “green zone”. For example, agreen zone may be a special type of environmental zone withenvironmental zone restrictions applied, such as only vehicles that meetcertain emission standards are permitted to be driven in the green zone,only vehicles which are identified as such by a special color-codedsticker may be allowed to enter in the green zone and the like. Vehiclesthat do not meet these standards may not be permitted inside theenvironmental zone.

The term “autonomous vehicle” may refer to any vehicle having autonomousdriving capabilities at least in some conditions. An autonomous vehicle,as used throughout this disclosure, may refer to a vehicle havingautonomous driving capabilities at least in some conditions. Theautonomous vehicle may also be known as a driverless car, robot car,self-driving car or autonomous car. For example, the vehicle may havezero passengers or passengers that do not manually drive the vehicle,but the vehicle drives and maneuvers automatically. There can also besemi-autonomous vehicles.

End of Definitions

Embodiments of the present disclosure may provide a system, a method anda computer program product for detecting an environmental zone andgenerating a time schedule of an environmental zone in a region forrouting.

Some embodiments provide a system and a method for detecting a presenceor an absence of the environmental zone in the region. Further, based onthe detection, navigation instructions for controlling the operation ofa vehicle may be provided. For example, a navigation instruction mayinclude providing a routing instruction to the vehicle (and the user) byproviding an alternate route for navigation of the vehicle. Thealternate route may be a route which does not include the environmentalzone. In some embodiments, the alternate route may be updated inreal-time based on dynamic detection of the environmental zone. Thedynamic detection of the environmental zone comprises updating acoverage area of the environmental zone dynamically, such as in realtime, based on a change in environmental conditions associated with thedetection of the environmental zone. The change in environmentalconditions may be determined based on a confidence value associated withthe environmental zone. The confidence value may be a numerical valuebetween 0 and 1 that indicates the likelihood of presence of theenvironmental zone in the region, with 0 meaning no environmental zone,1 meaning environmental zone active and 0.5 meaning 50% likely thatenvironmental zone is active (or present)

In some embodiments, controlling the operation of a vehicle may includeadjusting an emission level for the vehicle based on the detection ofthe environmental zone restriction in the region. For example, thevehicle may be equipped with an emission control system that may be ableto control emission of pollution causing gases from the vehicle on beingtriggered. The trigger may be provided on detection of the environmentalzone condition in the region and an indication that vehicle operationneeds to be controlled, such as by the navigation instruction.

Some embodiments provide a system and a method for providing a timeschedule for the environmental zone in the region. The time schedulecomprises such as data indicating either one of the presence or absenceof the environmental zone in the region for a sub-interval in aplurality of time intervals. The plurality of time intervals may each beof equal length, such as 1 hour, 30 min, 15 min etc., which may beconfigurable. Further the plurality of intervals may be used to divideeach day of the week into plurality of sub-intervals based on the lengthof each of the plurality of time intervals. For example, if the lengthof each time interval for the plurality of time intervals is chosen as 1hour, then each day is divided into 24 sub-intervals. Further, for eachsub-interval detection of the presence or absence of the environmentalzone is done for the region.

Thus, based on the systems and methods discussed herein, environmentalzone conditions may be dynamically detected, and a precise time schedulefor existence or non-existence of the environmental zone may also beprovided to the user. The user can get up to date information about theenvironmental zone conditions in a region, which are dynamically updatedin real time, and thus, provide efficient and accurate environmentalzone information. Further, since the time schedule is efficientlygenerated, a user can be informed about existence of such conditions ontheir planned navigation route well in time and can even be providednavigation instructions for efficient routing and vehicle operation.Also, since the information is timely and precise, the user can be savedfrom having to pay heavy fee in case they are about to enter a regionwith active environmental zone conditions and their vehicle is eitherhigh on emissions or does not have a sticker which permits the vehicleto enter into the environmental zone, such as a green zone. Also, thedetection of environmental zone, generation of time schedule and dynamicupdate of the environmental zone related information may be done by amap layer of a mapping service provider, thereby making the systems andmethods disclosed herein computationally efficient for the user andrequiring very less computational resources at the user end. The enduser, such as a consumer of an autonomous or semi-autonomous vehicle oran automobile maker of such vehicles may subscribe to the systems andmethods provided herein as a service provided by the mapping serviceprovider. These and other technical improvements of the systems andmethods disclosed herein may become apparent with the followingdescription of various embodiments described herein.

The system, the method, and the computer program product facilitatingdetection of an environmental zone and generating a time schedule of anenvironmental zone in a region are described with reference to FIG. 1 toFIG. 6A-6C.

FIG. 1 illustrates a schematic diagram of a network environment 100 of asystem 101 for detecting an environmental zone and generating a timeschedule of an environmental zone in a region, in accordance with anexample embodiment.

The system 101 may be communicatively coupled to a mapping platform 103,a user equipment 105 and an OEM (Original Equipment Manufacturer) cloud109 connected via a network 107. The mapping platform further comprisinga map database 103 a and a processing server 103 b. The componentsdescribed in the network environment 100 may be further broken down intomore than one component such as one or more sensors or application inuser equipment and/or combined in any suitable arrangement. Further, itis possible that one or more components may be rearranged, changed,added, and/or removed.

In an example embodiment, the system 101 may be embodied in one or moreof several ways as per the required implementation. For example, thesystem 101 may be embodied as a cloud based service or a cloud basedplatform. As such, the system 101 may be configured to operate outsidethe user equipment 105. However, in some example embodiments, the system101 may be embodied within the user equipment 105, for example as a partof an in-vehicle navigation system. In each of such embodiments, thesystem 101 may be communicatively coupled to the components shown inFIG. 1 to carry out the desired operations and wherever requiredmodifications may be possible within the scope of the presentdisclosure. In various embodiments, the system 101 may be a backendserver, a remotely located server, a cloud server or the like. In anembodiment, the system 101 may be the processing server 103 b of themapping platform 103 and therefore may be co-located with or within themapping platform 103. The system 101 may be implemented in a vehicle,where the vehicle may be an autonomous vehicle, a semi-autonomousvehicle, or a manually driven vehicle. Further, in one embodiment, thesystem 101 may be a standalone unit configured for detecting anenvironmental zone and generating a time schedule of an environmentalzone in a region. Alternatively, the system 101 may be coupled with anexternal device such as the autonomous vehicle.

The mapping platform 103 may comprise the map database 103 a for storingmap data and the processing server 103 b for carrying out processinginstructions. The map database 103 a may store node data, road segmentdata, link data, point of interest (POI) data, link identificationinformation, heading value records, environmental zone data, timeschedule data for the environmental zone or the like. In someembodiments, the map database 103 a comprises a map layer speciallyconfigured for storing environmental zone data. The environmental zonedata in the map layer further comprises time schedule data for theenvironmental zone. The time schedule data may comprise informationabout times of day when environmental zone restrictions are active atvarious regions defined in the map database 103 a. These regions may bedefined by geographic coordinate data for a location, map tile data,road segment data, link level data, lane level data and the like. Insome embodiments, the regions may be demarcated by polygons representingcoverage area for the environmental zone within the region, such aswithin a map tile.

In some embodiments, the map database 103 a further includes speed limitdata of each lane, cartographic data, routing data, and/or maneuveringdata. Additionally, the map database 103 a may store informationassociated with environmental zones in a region. The environmental zonesare established with the aim of improving air quality and the health ofthe residents living in the environmental zone. In an embodiment, eachenvironmental zone is assigned an environmental zone id, which may bestored in the map layer of the map database 103 a. And for eachenvironmental zone different environmental zone conditions and polygonsmay be stored in the map database 103 a which may be updated based onthe changes in the environmental zone in real time or in time epochs,which are predefined time intervals. Additionally, the map database 103a may be updated dynamically to cumulate real time traffic conditions.The real time traffic conditions may be collected by analyzing thelocation transmitted to the mapping platform 103 by many road usersthrough the respective user devices of the road users. In one example,by calculating the speed of the road users along a length of road, themapping platform 103 may generate a live traffic map, which is stored inthe map database 103 a in the form of real time traffic conditions. Inone embodiment, the map database 103 a may further store historicaltraffic data that includes travel times, average speeds and probe countson each road or area at any given time of the day and any day of theyear. In an embodiment, the map database 103 a may store the probe dataover a period for a vehicle to be at a link or road at a specific time.The probe data may be collected by one or more devices in the vehiclesuch as one or more sensors or image capturing devices or mobiledevices. In an embodiment, the probe data may also be captured fromconnected-car sensors, smartphones, personal navigation devices, fixedroad sensors, smart-enabled commercial vehicles, and expert monitorsobserving accidents and construction. In some embodiments, the probedata includes data related to environmental zones. For e.g. probevehicles equipped with one or more sensors may be configured to collectinformation about posted green zone signs on various roads. Probevehicles may also be equipped with special pollution sensors to detectreal time pollution levels and if the pollution level is greater than athreshold pollution level, report the environmental zone condition ofyes. Further, if the pollution level is less than the thresholdpollution level, report the environmental zone condition of no.

In some embodiments, data related to environmental zone, as stored inmap database 103 a is collected by consumer vehicles or end uservehicles. However, this data from consumer vehicles may first be sent tothe OEM cloud 109 for anonymization, and then the anonymized vehicledata is sent to the map database 103 a.

In some embodiments, data related to environmental zone, as collected byconsumer vehicles, is directly sent to the map database 103 a andanonymization is done in the map database 103 a itself.

According to some example embodiments, the map database 103 a may storedata related to segment data records such as node data, links orsegments representing roads, streets, or paths, as may be used incalculating a route or recorded route information for determination ofone or more personalized routes. The node data may be end points (e.g.,representing intersections) corresponding to the respective links orsegments of road segment data. The road link data and the node data mayrepresent a road network used by vehicles such as cars, trucks, buses,motorcycles, and/or other entities.

Optionally, the map database 103 a may contain path segment and nodedata records, such as shape points or other data that may representpedestrian paths, links or areas in addition to or instead of thevehicle road record data, for example. The road/link segments and nodescan be associated with attributes, such as geographic coordinates,street names, address ranges, speed limits, turn restrictions atintersections, and other navigation related attributes, as well as POIs,such as fueling stations, hotels, restaurants, museums, stadiums,offices, auto repair shops, buildings, stores, parks, etc. The mapdatabase 103 a may also store data about the POIs and their respectivelocations in the POI records.

The map database 103 a may additionally store data about places, such ascities, towns, or other communities, and other geographic features suchas bodies of water, mountain ranges, etc. Such place or feature data canbe part of the POI data or can be associated with POIs or POI datarecords (such as a data point used for displaying or representing aposition of a city). In addition, the map database 103 a may includeevent data (e.g., traffic incidents, construction activities, scheduledevents, unscheduled events, accidents, diversions etc.) associated withthe POI data records or other records of the map database 103 aassociated with the mapping platform 103. Optionally, the map database103 a may contain path segment and node data records or other data thatmay represent pedestrian paths or areas in addition to or instead of theautonomous vehicle road record data.

The map database 103 a may be maintained by a content provider e.g., amap developer. By way of example, the map developer may collectgeographic data to generate and enhance the map database 103 a. Theremay be different ways used by the map developer to collect data. Theseways may include obtaining data from other sources, such asmunicipalities or respective geographic authorities. In addition, themap developer may employ field personnel to travel by vehicle alongroads throughout the geographic region to observe features and/or recordinformation about them, for example. Also, remote sensing, such asaerial or satellite photography, may be used to generate map geometriesdirectly or through machine learning as described herein.

In some embodiments, the map database 103 a may be a master map databasestored in a format that facilitates updating, maintenance anddevelopment. For example, the master map database or data in the mastermap database may be in an Oracle spatial format or other spatial format,such as for development or production purposes. The Oracle spatialformat or development/production database may be compiled into adelivery format, such as a geographic data files (GDF) format. The datain the production and/or delivery formats may be compiled or furthercompiled to form geographic database products or databases, which may beused in end user navigation devices or systems.

For example, geographic data may be compiled (such as into a platformspecification format (PSF) format) to organize and/or configure the datafor performing navigation-related functions and/or services, such asroute calculation, route guidance, map display, speed calculation,distance and travel time functions, and other functions, by a navigationdevice, such as by the user equipment 105. The navigation-relatedfunctions may correspond to vehicle navigation, pedestrian navigation orother types of navigation. The compilation to produce the end userdatabases may be performed by a party or entity separate from the mapdeveloper. For example, a customer of the map developer, such as anavigation device developer or other end user device developer, mayperform compilation on a received map database in a delivery format toproduce one or more compiled navigation databases.

As mentioned above, the map database 103 a may be a master geographicdatabase, but in alternate embodiments, the map database 103 a may beembodied as a client-side map database and may represent a compilednavigation database that may be used in or with end user equipment suchas the user equipment 105 to provide navigation and/or map-relatedfunctions. For example, the map database 103 a may be used with the userequipment 105 to provide an end user with navigation features. In such acase, the map database 103 a may be downloaded or stored locally(cached) on the user equipment 105.

The processing server 103 b may comprise processing means, andcommunication means. For example, the processing means may comprise oneor more processors configured to process requests received from the userequipment 105. The processing means may fetch map data from the mapdatabase 103 a and transmit the same to the user equipment 105 via OEMcloud 109 in a format suitable for use by the user equipment 105. Inanother embodiment, the data collected from the vehicles is transmittedto the OEM cloud 109 for anonymization and then back to mapping platform103 for further processing and aggregation. In one or more exampleembodiments, the mapping platform 103 may periodically communicate withthe user equipment 105 via the processing server 103 b to update a localcache of the map data stored on the user equipment 105. Accordingly, insome example embodiments, the map data may also be stored on the userequipment 105 and may be updated based on periodic communication withthe mapping platform 103.

In some example embodiments, the user equipment 105 may be any useraccessible device such as a mobile phone, a smartphone, a portablecomputer, and the like that are portable in themselves or as a part ofanother portable/mobile object such as a vehicle. The user equipment 105may comprise a processor, a memory and a communication interface. Theprocessor, the memory and the communication interface may becommunicatively coupled to each other. In some example embodiments, theuser equipment 105 may be associated, coupled, or otherwise integratedwith a vehicle of the user, such as an advanced driver assistance system(ADAS), a personal navigation device (PND), a portable navigationdevice, an infotainment system and/or other device that may beconfigured to provide route guidance and navigation related functions tothe user. In such example embodiments, the user equipment 105 maycomprise processing means such as a central processing unit (CPU),storage means such as onboard read only memory (ROM) and random accessmemory (RAM), acoustic sensors such as a microphone array, positionsensors such as a GPS sensor, gyroscope, a LIDAR sensor, a proximitysensor, motion sensors such as accelerometer, a pollution sensor, acamera or other image sensors, a display enabled user interface such asa touch screen display, and other components as may be required forspecific functionalities of the user equipment 105. Additional,different, or fewer components may be provided. For example, the userequipment 105 may be configured to execute and run mobile applicationssuch as a messaging application, a browser application, a navigationapplication, and the like. In one embodiment, at least one userequipment such as the user equipment 105 may be directly coupled to thesystem 101 via the network 107. For example, the user equipment 105 maybe a dedicated vehicle (or a part thereof) for gathering data fordevelopment of the map data in the database 103 a. In some exampleembodiments, at least one user equipment such as the user equipment 105may be coupled to the system 101 via the OEM cloud 109 and the network107. For example, the user equipment 105 may be a consumer vehicle (or apart thereof) and may be a beneficiary of the services provided by thesystem 101. In some example embodiments, the user equipment 105 mayserve the dual purpose of a data gatherer and a beneficiary device. Theuser equipment 105 may be configured to capture sensor data associatedwith a road which the user equipment 105 may be traversing. The sensordata may for example include pollution level information in an areacollected by pollution sensors in the vehicles. In another embodiment,the sensor data may be image data of road objects, road signs, or thesurroundings (for example buildings). The sensor data may refer tosensor data collected from a sensor unit in the user equipment 105. Inaccordance with an embodiment, the sensor data may refer to the datacaptured by the vehicle using sensors.

The network 107 may be wired, wireless, or any combination of wired andwireless communication networks, such as cellular, Wi-Fi, internet,local area networks, or the like. In one embodiment, the network 107 mayinclude one or more networks such as a data network, a wireless network,a telephony network, or any combination thereof. It is contemplated thatthe data network may be any local area network (LAN), metropolitan areanetwork (MAN), wide area network (WAN), a public data network (e.g., theInternet), short range wireless network, or any other suitablepacket-switched network, such as a commercially owned, proprietarypacket-switched network, e.g., a proprietary cable or fiber-opticnetwork, and the like, or any combination thereof. In addition, thewireless network may be, for example, a cellular network and may employvarious technologies including enhanced data rates for global evolution(EDGE), general packet radio service (GPRS), global system for mobilecommunications (GSM), Internet protocol multimedia subsystem (IMS),universal mobile telecommunications system (UMTS), etc., as well as anyother suitable wireless medium, e.g., worldwide interoperability formicrowave access (WiMAX), Long Term Evolution (LTE) networks (for e.g.LTE-Advanced Pro), 5G or 6G New Radio networks, ITU-IMT 2020 networks,code division multiple access (CDMA), wideband code division multipleaccess (WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN),Bluetooth, Internet Protocol (IP) data casting, satellite, mobile ad-hocnetwork (MANET), and the like, or any combination thereof. In anembodiment the network 107 is coupled directly or indirectly to the userequipment 105 via OEM cloud 109. In an example embodiment, the systemmay be integrated in the user equipment 105. In an example, the mappingplatform 103 may be integrated into a single platform to provide a suiteof mapping and navigation related applications for OEM devices, such asthe user devices and the system 101. The system 101 may be configured tocommunicate with the mapping platform 103 over the network 107. Thus,the mapping platform 103 may enable provision of cloud-based servicesfor the system 101, such as, anonymization of observations in the OEMcloud 109 in batches or in real-time.

FIG. 2 illustrates a block diagram of a system 101 for detecting anenvironmental zone and generating a time schedule of an environmentalzone in a region, in accordance with an example embodiment. The system101 may include a processing means such as at least one processor 201(hereinafter, also referred to as “processor 201”), storage means suchas at least one memory 203 (hereinafter, also referred to as “memory203”), and a communication means such as at least one communicationinterface 205 (hereinafter, also referred to as “communication interface205”). The processor 201 may retrieve computer program code instructionsthat may be stored in the memory 203 for execution of the computerprogram code instructions.

The processor 201 may be embodied in several different ways. Forexample, the processor 201 may be embodied as one or more of varioushardware processing means such as a coprocessor, a microprocessor, acontroller, a digital signal processor (DSP), a processing element withor without an accompanying DSP, or various other processing circuitryincluding integrated circuits such as, for example, an ASIC (applicationspecific integrated circuit), an FPGA (field programmable gate array), amicrocontroller unit (MCU), a hardware accelerator, a special-purposecomputer chip, or the like. As such, in some embodiments, the processor201 may include one or more processing cores configured to performindependently. A multi-core processor may enable multiprocessing withina single physical package. Additionally, or alternatively, the processor201 may include one or more processors configured in tandem via the busto enable independent execution of instructions, pipelining and/ormultithreading.

In some embodiments, the processor 201 may be configured to provideInternet-of-Things (IoT) related capabilities to users of the system101, where the users may be a traveler, a rider, a pedestrian, and thelike. In some embodiments, the users may be or correspond to anautonomous or a semi-autonomous vehicle. The IoT related capabilitiesmay in turn be used to provide smart navigation solutions by providingreal time updates to the users to take pro-active decision onturn-maneuvers, lane changes, overtaking, merging and the like, big dataanalysis, and sensor-based data collection by using the cloud basedmapping system for providing navigation recommendation services to theusers. The system 101 may be accessed using the communication interface205. The communication interface 205 may provide an interface foraccessing various features and data stored in the system 101.

Additionally, or alternatively, the processor 201 may include one ormore processors capable of processing large volumes of workloads andoperations to provide support for big data analysis. In an exampleembodiment, the processor 201 may be in communication with the memory203 via a bus for passing information among components coupled to thesystem 101.

The memory 203 may be non-transitory and may include, for example, oneor more volatile and/or non-volatile memories. In other words, forexample, the memory 203 may be an electronic storage device (forexample, a computer readable storage medium) comprising gates configuredto store data (for example, bits) that may be retrievable by a machine(for example, a computing device like the processor 201). The memory 203may be configured to store information, data, content, applications,instructions, or the like, for enabling the apparatus to carry outvarious functions in accordance with an example embodiment of thepresent invention. For example, the memory 203 may be configured tobuffer input data for processing by the processor 201. As exemplarilyillustrated in FIG. 2, the memory 203 may be configured to storeinstructions for execution by the processor 201. As such, whetherconfigured by hardware or software methods, or by a combination thereof,the processor 201 may represent an entity (for example, physicallyembodied in circuitry) capable of performing operations according to anembodiment of the present invention while configured accordingly. Thus,for example, when the processor 201 is embodied as an ASIC, FPGA or thelike, the processor 201 may be specifically configured hardware forconducting the operations described herein. Alternatively, as anotherexample, when the processor 201 is embodied as an executor of softwareinstructions, the instructions may specifically configure the processor201 to perform the algorithms and/or operations described herein whenthe instructions are executed. However, in some cases, the processor 201may be a processor specific device (for example, a mobile terminal or afixed computing device) configured to employ an embodiment of thepresent invention by further configuration of the processor 201 byinstructions for performing the algorithms and/or operations describedherein. The processor 201 may include, among other things, a clock, anarithmetic logic unit (ALU) and logic gates configured to supportoperation of the processor 201.

The communication interface 205 may comprise input interface and outputinterface for supporting communications to and from the user equipment105 or any other component with which the system 101 may communicate.The communication interface 205 may be any means such as a device orcircuitry embodied in either hardware or a combination of hardware andsoftware that is configured to receive and/or transmit data to/from acommunications device in communication with the user equipment 105. Inthis regard, the communication interface 205 may include, for example,an antenna (or multiple antennae) and supporting hardware and/orsoftware for enabling communications with a wireless communicationnetwork. Additionally, or alternatively, the communication interface 205may include the circuitry for interacting with the antenna(s) to causetransmission of signals via the antenna(s) or to handle receipt ofsignals received via the antenna(s). In some environments, thecommunication interface 205 may alternatively or additionally supportwired communication. As such, for example, the communication interface205 may include a communication modem and/or other hardware and/orsoftware for supporting communication via cable, digital subscriber line(DSL), universal serial bus (USB) or other mechanisms.

FIG. 3A illustrates an exemplary scenario 300 a for detecting anenvironmental zone in a region and generating a time schedule of anenvironmental zone in the region, in accordance with an exampleembodiment. According to one example embodiment, a vehicle 301 (such asthe vehicle/user equipment 105) may be traveling on a road 303. The road303 may be part of a way leading the vehicle 301 from a source locationto a destination location. In one example, the road 303 may be a roadthat may be a part of an environmental zone or green zone. To that end,the road 303 may be an entry path signaling the beginning of theenvironmental zone or an exit path signaling the end of theenvironmental zone. The information about the start or end of theenvironmental zone, such as the green zone may be shown by road sign305, such as by displaying the label “green zone” on the road sign 305.The road sign 305 shown in FIG. 3A is a banner that may indicate startof the green zone and a similar road sign may be placed at a place onthe road 303 to indicate the end of the green zone, as definedpreviously.

In an example embodiment, the vehicle 301 may request for a routebetween two locations and the road 303 may be a part of the requestedroute. Further, the road sign 305 may indicate that the environmentalzone starts from this point. In an embodiment, the vehicle 301 maydetect the region as environmental zone by observing the road sign 305using the vehicle's onboard sensors. For example, the vehicle 301 maydetect the region as environmental zone by observing the road sign 305using a camera, a LIDAR sensor, a depth sensor, a GPS sensor and thelike. In another embodiment, the vehicle 301 may detect that the regionis an environmental zone based on the sensor data collected from one ormore other sensors in the vehicle 301. For example, pollution sensorsmay detect that the pollution level in the region including the road 303is above a threshold value and hence the region is to be considered asenvironmental zone. The system 101 may be invoked upon receipt of therequest for a route for navigation. The system 101 may also be invokedfor providing navigational alert to the vehicle 301 automatically, ondetection of the environmental zone in the region including the road303. Based on the request from vehicle 301, the system 101 may beconfigured to determine coverage and time schedule of the environmentalzone. Alternately, the environmental zone may already be indicated inthe route for the vehicle 301 and the system 101 may be invoked togenerate alternate route for the vehicle 301 on the basis of thedetected environmental zone and the coverage area of the environmentalzone. Irrespective of the way the system 101 is invoked, the system 101may provide measures for detecting an environmental zone and generatinga time schedule of an environmental zone in a region.

On being invoked, the system 101 may obtain at least one observationassociated with the environmental zone in the region, such as on theroad 303. In an embodiment, at least one observation may be reported byplurality of vehicles in the environmental zone. For example, onevehicle may obtain the environmental zone observation at one location inthe region and another vehicle may report the environmental zoneobservation at other location in the region. Further, the at least oneobservation may be reported at a first time epoch, say between 1 AM-2AM, at a first location by a first vehicle. Further, a secondobservation is reported by the same first vehicle at a second locationin a second time epoch, say between 2 AM-3 AM. Like this, a plurality ofobservations associated with the environmental zone conditions may beobtained. Further, the time epoch may correspond to a time interval ofany length or duration, such as 1 hour, 30 minutes, 15 minutes, 5minutes and the like, based on the frequency of update required forenvironmental zone information.

FIG. 3B illustrates an exemplary map tile 300 b showing obtainedplurality of observations for environmental zone at plurality oflocations using a vehicle sensor data. The dots in the map tile 300 bshow the locations for which environmental zone observations wereobtained within a region defined by the map tile. For example, the maptile 300 b corresponds to the region Stuttgart in Germany, and the dotsrepresent locations where a green zone road sign was observed by avehicle using their onboard sensors, such as a camera. The plurality ofobservations in FIG. 3B were collected for a period of 24 hours for theregion shown in map tile 300 b.

In an embodiment, the region may correspond to a single map tile, suchas the map tile 300 b, or multiple map tiles, a geographic area, a POI,a street, a lane and the like. In an embodiment, the at least oneobservation may include at least one of a positive observation and anegative observation, a detailed description of which is provided nextwith reference to FIGS. 4A-4C. The positive observation is associatedwith a first determination of presence of environmental zone in theregion and the negative observation is associated with a seconddetermination of absence of environmental zone in the region. Further,each of the at least one positive observation and the at least onenegative observation may be associated with a time interval, for examplean epoch of predefined length discussed earlier, associated with eachday in a week. The system 101 may further determine the confidence valueassociated with the at least one observation in the region. Theconfidence value is a numerical value between 0 and 1, which representsa degree of confidence and likelihood attributed to the correctness andaccuracy of the observation to which it is attributed. For example, aconfidence value of 0.1 means 10% confidence and likeliness of theobservation being correct and accurate, while a confidence value of 0.9would mean 90% confidence and likeliness of the observation beingcorrect.

In some embodiments, the system 101 may continuously monitor the changein confidence value to determine the coverage of the environmental zoneand thus, environmental zone data may be updated dynamically in nearreal-times. Based on the change in confidence value, the system 101 maydetermine the presence or absence of environmental zone in the regionand accordingly update the coverage area and its extents/limits todepict up to date coverage area for the environmental zone. Also, basedon the time interval information available for the coverage area of theenvironmental zone in the region, a time schedule for the environmentalzone may also be generated.

For example, the system 101 may obtain ten observations in a region. Andwhile continuously monitoring the change in confidence value, the system101 may determine that the confidence value of three observations is lowand the area associated with those three observations is no longer underenvironmental zone. The system 101 may further update about the coverageof the environmental zone in the map database, a detailed description ofwhich is provided next with reference to FIGS. 4A-4D.

In an embodiment, the system 101 may generate the time schedule of theenvironmental zone in the region. The system 101 may further obtain, fora predefined time interval, a plurality of observations associated withthe environmental zone in the region. In an embodiment, the predefinedtime interval may be the time epoch as discussed previously. The system101 may further determine the at least one positive observation and atleast one negative observation associated with the environmental zone inthe region for the predefined time interval. Like this, the system 101may obtain a plurality of observations for the environmental zone in theregion during the time interval defined by the time epoch. Theseplurality of observations may be observed by a plurality of vehiclesduring the defined time interval, for the region under considerations.The plurality observations may include both the types of observations:plurality of positive observations, when some plurality of vehiclesreport environmental zone condition as present or “YES”; and pluralityof negative observations, when some plurality of vehicles reportenvironmental zone condition as absent or “NO”. The system 101 mayfurther aggregate the plurality of positive observations to determine anaggregated positive observation value and aggregate the plurality ofnegative observations to determine the aggregated negative observationvalue. Further, the system 101 determines the confidence value based onthe aggregated positive observation value and the aggregated negativeobservation value. Based on the confidence value, the system 101 maydetect either one of a presence or an absence of the environmental zonebased on the confidence value and generates the time schedule of theenvironmental zone based on the detection. The calculation of confidencevalue in this manner is further explained in detail in FIG. 4A-4D. FIGS.4A-4B illustrate exemplary tables for obtaining observations related toan environmental zone in a region.

FIG. 4A illustrates an exemplary table 400 a of positive observationsassociated with the environmental zone, for a predefined time interval,such as a week, for a location. In an embodiment, the at least onepositive observation is the observation when the pollution level in theregion is greater than a threshold value or when a vehicle has observeda posted green zone road sign. In FIG. 4A, there are plurality ofpositive observations for environmental zone, which are captured for apredefined time interval by plurality of vehicles.

Table 400 a includes top row containing days of a week in differentcolumns, and first column containing hours of a day. Hour 0 represents0th hour, which is 12 AM-1 AM, which forms row 1 of the table 400 a. Forexample, in the third column of table 400 a, row 2 shows the number ‘5’,which means five positive observations were obtained on Mon forsub-interval 1 AM to 2 AM by vehicles for a particular region, which mayfurther be specified to be a location. This may also be represented asOBS_YES(Mon,2)=5, where OBS_YES means observations with environmentalzone detection output as “YES”, that is environmental zone is present.Similarly, in the fifth column of table 400 a, the plurality of positiveobservations obtained by vehicles on Wednesday for sub-interval 3 AM-4AM is zero, which means OBS_YES(Wed, 4)=0, that is to say, noenvironmental zone was observed by any vehicle that crossed the regionbetween 3 AM and 4 AM on Wednesday. Further, the system 101 may sendthese observations for anonymization to OEM cloud 109. The OEM cloud 109may perform anonymization algorithms for the plurality of positiveobservations and then send the observations to the map database 103 a.Alternately, the map database 103 a may itself anonymize the pluralityof positive observations before using them for further processing.

FIG. 4B illustrates an exemplary table 400 b of negative observationsassociated with the environmental zone, for a predefined time interval,such as a week, for a location. In an embodiment, the at least onenegative observation is the observation when the pollution level in theregion is lesser than a threshold value or when a vehicle has notobserved any posted green zone road sign. In this case, vehicularemissions are under permissible limits, and need to be considered whilemonitoring entry or exit of vehicles in any regions. In FIG. 4B, thereare plurality of negative observations for environmental zone, which arecaptured for a predefined time interval by plurality of vehicles.

In Table 400 b, the structure and organization of data is like table 400a. Thus, table 400 b represents, for example, two negative observationson Monday from 4 AM to 5 AM. This may be represented as OBS_NO(Mon,5)=2,where OBS_NO means observations with environmental zone detection outputas “NO”, that is environmental zone is not present. The OEM cloud 109may perform anonymization algorithms for the plurality of negativeobservations in table 400 b and then send these plurality of negativeobservations to the map database 103 a. Alternately, the map database103 a may itself anonymize the plurality of negative observations beforeusing them for further processing

In some embodiments, the plurality of positive observations in table 400a and the plurality of negative observations in table 400 b may be usedto continuously update the map database 103 a. For example, in the mapdatabase 103 a there may be the map layer storing the environmental zonerelated data. This data may include the tables 400 a and 400 b. Further,whenever more vehicles report at least one positive observation ornegative observation, the tables 400 a and 400 b may be updated in realtime.

FIG. 4C illustrates a method 400 c for updating the tables 400 a and 400b shown in FIGS. 4A and 4B.

The method 400 c begins at step 400 c 1 when a vehicle passes through anenvironmental zone in a region. At step 400 c 3, the vehicle obtains atleast one observation for the environmental zone in the region. Thisobservation may be obtained using vehicle's onboard sensors. The onboardsensors may either report a posted green zone sign or may report apollution level estimation in the region. Based on the observationreported by the vehicle at 400 c 3, two possibilities may arise. If theenvironmental zone condition is detected, then at 400 c 5, thecorresponding count in the table 400 a for positive observations, alsoreferred to as Obs Environmental Zone_YES is incremented. However, ifenvironmental zone condition is not detected, then at 400 c 7, thecorresponding count in the table 400 a for negative observations, alsoreferred to as Obs Environmental Zone_NO is incremented.

The cell to be updated in table 400 a or 400 b is identified based ontwo criteria: 1) identified region/location, and 2) time of day (andthus corresponding sub-interval for identifying the hour of the day).

Thus, using the method 400 c, the map database 103 a may be updated inreal time with environmental zone data. Further, the method 400 cenables dynamic update of the environmental zone data in the mapdatabase 103 a, thereby making the system 101 highly accurate, reliable,up to date and efficient. Not only this, the continuous monitoring ofenvironmental zone information in this manner makes the system 101highly dynamic and robust. This updated information about theenvironmental zone may be used to calculate an updated confidence valuefor the environmental zone, which may be further used to provide updatednavigational instructions to the vehicle 301 traversing through theregion, such as the road 303.

FIG. 4D illustrates an exemplary scenario in a table 400 d showingcalculation of the confidence value at different days and time epochsfor a location. After obtaining the plurality of positive observationand the plurality of negative observations, the system 101 may furtheraggregate the plurality of positive observation and the plurality ofnegative observation to compute the confidence value. The aggregation ofthe plurality of positive observations and plurality of negativeobservations to determine the confidence value is shown as

$\begin{matrix}\frac{{{OBS\_ YES}( {{day},{hour}} )} + 1}{{{OBS\_ YES}( {{day},{hour}} )} + {{OBS\_ NO}( {{day},{hour}} )} + 2} & (1)\end{matrix}$

where, OBS_YES denotes the yes observation or positive observation on aparticular day and in a time epoch (or sub-interval) and OBS_No denotesthe no observation or negative observation at same location and in thesame time epoch. The equation (1) calculated for determining confidencevalue may be based on one or more different frameworks. For example, analgorithm associated with Bayesian Framework may be used to computeconfidence value. In an embodiment, the confidence value on Monday forthe sub-interval from 1 AM to 2 AM is calculated based on the equation(1) and using the plurality of positive observations in table 400 a andthe plurality of negative observations in table 400 b. The number ofaggregated plurality of positive observations from table 400 a in thistime epoch is five. Similarly, the number of aggregated plurality ofnegative observations from table 400 b for this time epoch is zero.Therefore, using these observations in equation (1), the confidencevalue of the environmental zone is 85.71%. Similarly, the confidencevalue on Monday from 4 AM to 5 AM is calculated based on the equation(1) and using the plurality of positive observations in table 400 a andthe plurality of negative observations in table 400 b. The number ofaggregated plurality of positive observations from table 400 a in thistime epoch is zero. Similarly, the number of negative observations fromtable 400 b in this time epoch is two. Therefore, using these values inequation (1), the confidence value of the environmental zone is 25%. Inan embodiment, in case when the value of positive observation andnegative observation is zero, the system 101 may be configured to takeprior probability for both observation-yes and observation-no to be 0.5and 0.5. In an embodiment, if the one or more observations are missingfor a particular time epoch, then the system 101 may determine theconfidence values of missing observation based on the historicalconfidence value associated with the plurality of observations.

The system 101 may further detect either one of the presence or absenceof the environmental zone in the region for each sub-interval (hour ofday) in the plurality of time intervals, wherein each sub-intervalcorresponds to the predefined length/epoch of time interval, such as 1hour, 30 min, 15 min etc. The system 101 may be configured to generatethe time schedule of the environmental zone in the region and indicatesthe presence or absence of the environmental zone in the region for eachsub-interval in the plurality of time intervals for each day in a weekusing the calculations done as illustrated in table 400 d. For example,the system 101 may obtain the plurality of observations at a locationfor a week. The week may be divided into days and day further intohours. The system 101 may further determine the plurality of positiveobservations and/or the plurality of negative observations for theregion. By aggregating the plurality of positive observations and theplurality of negative observations as shown in table 400 d, the system101 may determine the confidence value for the environmental zone. Forexample, on Monday at 1 pm the confidence value may be 0.9 for thelocation and on Saturday the confidence value may be 0.2.

In some embodiments, the system 101 may compare the calculatedconfidence value to a threshold confidence value. The thresholdconfidence value may be customizable based on a variety of parameters,such as environmental pollution levels, type of region (for exampleschool, hospital etc.), type of vehicle, weather, and the like. Based onthe comparison of the confidence value with the threshold confidencevalue, the system 101 may generate the time schedule for theenvironmental zone. For example, if the threshold confidence value isset to be 0.4, then the system 101 may detect that on Monday at 1 pm,the environmental zone is present whereas on Saturday at 1 pm, theenvironmental zone is absent for the same location. In this way, thesystem 101 may generate the time schedule.

The system 101 may further provide routing and navigational assistanceto the vehicles in a region. The system 101 may obtain route informationfor at least one vehicle. Based on the map data and route information,the system 101 may determine confidence values associated with differentlocations on the route in the region. Further, the system 101 maydetermine a coverage area for the environmental zone based on thedetermined confidence values for the different locations and provide theroute navigational instructions to the vehicle.

FIGS. 5A-5B illustrates an exemplary representation for detecting anenvironmental zone, in accordance with one or more example embodiments.FIGS. 5A-5B are explained in conjunction with FIGS. 3A-3B and FIGS.4A-4D. In FIG. 5A, there is shown a region 500 a with multiple tiles. Inthe region 500 a, area bounded by dots 501 shows the coverage areaassociated with an environmental zone, with points in it showingplurality of observations. Similarly, in FIG. 5B, there is shown aregion 500 b (which is same as region 500 a) with multiple tiles and 503is the updated coverage area associated with the environmental zone inthe region 500 b, with points in it showing plurality of observations.

As explained previously, the system 101 may continuously monitor thechange in confidence value associated with the environmental zones withcoverage areas 501 or 503, and after determining the confidence valuethe system 101 may detect an updated coverage area associated with theenvironmental zone. For example, in FIG. 5A, the system 101 may detectthe polygon shape of 501 based on the confidence value associated withthe plurality of observations, whereas the polygon shape associated withthe coverage area may change to 503 in FIG. 5B based on the change inconfidence value associated with the plurality of observations.

FIGS. 6A-6C illustrate flow diagrams of different method embodiments fordetecting an environmental zone and generating a time schedule of anenvironmental zone in a region, in accordance with an exampleembodiment. It will be understood that each block of the flow diagram ofmethods 600 a-600 c may be implemented by various means, such ashardware, firmware, processor, circuitry, and/or other communicationdevices associated with execution of software including one or morecomputer program instructions. For example, one or more of theprocedures described above may be embodied by computer programinstructions. In this regard, the computer program instructions whichembody the procedures described above may be stored by a memory 203 ofthe system 101, employing an embodiment of the present invention andexecuted by a processor 201. As will be appreciated, any such computerprogram instructions may be loaded onto a computer or other programmableapparatus (for example, hardware) to produce a machine, such that theresulting computer or other programmable apparatus implements thefunctions specified in the flow diagram blocks. These computer programinstructions may also be stored in a computer-readable memory that maydirect a computer or other programmable apparatus to function in aparticular manner, such that the instructions stored in thecomputer-readable memory produce an article of manufacture the executionof which implements the function specified in the flowchart blocks. Thecomputer program instructions may also be loaded onto a computer orother programmable apparatus to cause a series of operations to beperformed on the computer or other programmable apparatus to produce acomputer-implemented process such that the instructions which execute onthe computer or other programmable apparatus provide operations forimplementing the functions specified in the flow diagram blocks.

Accordingly, blocks of the flow diagram support combinations of meansfor performing the specified functions and combinations of operationsfor performing the specified functions for performing the specifiedfunctions. It will also be understood that one or more blocks of theflow diagram, and combinations of blocks in the flow diagram, may beimplemented by special purpose hardware-based computer systems whichperform the specified functions, or combinations of special purposehardware and computer instructions. The methods 600 a-600 c illustratedby the flowchart diagram of FIGS. 6A-6C is for detecting anenvironmental zone and generating a time schedule of an environmentalzone in a region. Fewer, more, or different steps may be provided.

In accordance with method 600 a, at step 600 a 1, the method 600 acomprises obtaining at least one observation associated with theenvironmental zone in a region. The method further comprises obtainingthe at least one observation associated with the environmental zone inthe region based on at least one of a plurality of road signs, one ormore pollution sensors, and one or more other sensors in a vehicle. Insome embodiments, the system 101 obtains at least one observation foreach time epoch. Further the system 101 obtains at least one observationfrom the first vehicle. Later, the system 101 may obtain the at leastone observation for the second vehicle. In this manner a plurality ofobservations associated with the environmental zone may be obtained. Theplurality of observations further comprise a plurality of positiveobservations and a plurality of negative observations, wherein each ofthe plurality of positive observations are associated with a firstdetermination of presence of environmental zone in the region, and eachof the plurality of negative observations are associated with a seconddetermination of absence of environmental zone in the region.

At step 600 a 3, the method 600 a comprises determining a confidencevalue associated with the at least one observation. For example, for anobservation taken for the sub-interval 1 AM-2 AM, data from tables 400 aand 400 b may updated, and then a confidence value may be calculatedusing the formula in equation (1). The same determination may be donefor the plurality of observations in the region. Further, the determinedconfidence value may be stored in the map database 103 a. Further, thestored confidence value may be used by different applications like forroute determination, re-routing of a vehicle, controlling vehicularemissions and the like. Also, the confidence value is updated in realtime based on the updated observations. For example, while detecting theenvironmental zone and the coverage area, the system 101 may update theconfidence value of a particular location when the system 101 determinesthat the confidence value of that location has changed.

In some embodiments, the method 600 a further comprises determining theconfidence value associated with the plurality of observations byaggregating the plurality of positive observations in the region andaggregating the plurality of negative observations in the region, anddetermining the confidence value based on the aggregated plurality ofpositive observations and the aggregated plurality of negativeobservations. This is shown in table 400 d, where aggregated positiveobservation value and aggregated negative observation value is input tothe formula in equation (1) in some embodiments, and the result of thecalculation provides the confidence value for environmental zone for aregion, such as area 501 shown in map 500 a, for a particular timesub-interval, such as 3 AM-4 AM. The method 600 a further comprisesdetermining the confidence value by continuously monitoring a change inconfidence value associated with each of the plurality of observationsin the region in real time.

At step 600 a 5, the method 600 a comprises detecting the environmentalzone in the region based on the determined confidence value associatedwith the plurality of observations in the region, wherein detectingcomprises determining either one of a presence or an absence of theenvironmental zone in the region. For example, the vehicle 301travelling on road 303 may detect the region as environmental zone ifthe confidence value associated with the observation is greater than athreshold confidence value. As explained in FIG. 5A, the system 101 maydetect that the region is an environmental zone on Monday from 1 AM to 2AM as the confidence value is 85.71%. In this case, the thresholdconfidence value may be 40% for exemplary purpose. Similarly, the system101 may detect that the region is not an environmental zone when theconfidence value associated with the observation is less than thethreshold confidence value. After detecting the environmental zone, thesystem 101 may also update the coverage of the environmental zone in themap database. For example, in FIG. 5A the coverage area is shown aspolygon shape 501 and similarly in FIG. 5B the coverage area is shown aspolygon shape 503 which is different from 501.

FIG. 6B illustrates another exemplary method 600 b for generating a timeschedule for an environmental zone in a region, according to an exampleembodiment.

The method 600 b comprises, at step 600 b 1, obtaining, for a predefinedtime interval, at least one observation associated with theenvironmental zone in the region. In some embodiments, the mappingplatform 103 includes one or more processors 103 b, which are configuredfor obtaining the at least one observation from the vehicle 301, butafter anonymization. The anonymization may either be done by the OEMcloud 109, or by the mapping platform 103 itself. Further, at least oneobservation may be associated with the region, such as the road 303, andfor a predefined time interval, such as any of the sub-intervalsincluded in tables 400 a or 400 b. Each such observation is used topopulate corresponding table 400 a or 400 b, and thus, in this manner aplurality of observations is obtained from a plurality of vehicles.

The plurality of observations obtained in this manner include aplurality of positive observations associated with the environmentalzone, such as in table 400 a, in the region for the predefined timeinterval; and a plurality of negative observations associated with theenvironmental zone, such as in table 400 b, in the region for thepredefined time interval.

Further, the one or more processors 103, are further configured toaggregate, for the region and the predefined time interval, both theplurality of positive observations and the plurality of negativeobservations to determine a corresponding aggregated positiveobservation value and a corresponding aggregated negative observationvalue.

The method 600 b further comprises, at step 600 b 3, determining, forthe predefined time interval, a confidence value associated with the atleast one observation. The confidence value is determined based on theaggregated positive observation value and the aggregated negativeobservation value. Further, based on the confidence value, the one ormore processors 103 b executing the method 600 b are configured todetect, for the region and the predefined time interval, either one of apresence or an absence of the environmental zone and generate the timeschedule of the environmental zone based on the detection.

Further, the method 600 b comprises at step 600 b 5, the one or moreprocessors in the system 101 are configured to generate a time schedulefor the environmental zone in the region based on the determinedconfidence value and the predefined time interval. As explained in FIG.4D, the confidence value on Monday from 1 AM to 2 AM is 85.71%,therefore the system 101 may determine the region on Monday from 1 AM to2 AM as environmental zone. Similarly, the confidence value on Mondayfrom 4 AM to 5 AM is 25%, therefore the system 101 may determine thatthe region is not an environmental zone on Monday from 4 AM to 5 AM.Therefore, based on this information, the system 101 may generate thetime schedule for the environmental zone in the region.

In some embodiments, the generated time schedule may be used to predictthe existence or non-existence of the environmental zone in the region.

In some embodiments, the generated time schedule is stored in theenvironmental zone related map layer of the map database 103 a and isfurther used to update the map layer for missing data related toplurality of observations for a region where observations are notavailable. In such cases, positive and negative observations at nearbylocations of the region where such observations are not available, canbe used to replenish the missing information at the candidate locationusing a threshold constraint on distance (e.g. 2 km). From theenvironmental zone related map layer map layer, observations of nearbyregion may be obtained and given a weight. These are considered asimplicit weighted observations. The weight of these implicitobservations could be continuous, between 0 and 1, but depends on thedistance of the candidate location to the real observations using adecay function. The implicit observations may be continuous values ormay be Boolean values. After replenishing the map layer with implicitobservations, the updated confidence value may be calculated, andfurther updated time schedule may be generated.

In some embodiments, for missing observations, the previously computedconfidence for the previous sub-interval is used with a time decay. Thetime decay parameters may be configurable and can be tuned according tovehicle penetration or map attributes, such as functional class,URBAN/RURAL flag, and the like.

In some embodiments, the time schedule and coverage area of theenvironmental zone determined using any of the methods 600 a or 600 bdiscussed above may be used to provide navigation assistance to thevehicle 301.

FIG. 6C illustrates another exemplary method 600 c for providing routenavigation instructions to one or more vehicles in a region, inaccordance with an exemplary embodiment.

The method 600 c comprises, at step 600 c 1, obtaining route informationfor navigation of at least one vehicle in a region. For example, thevehicle 301 may request for a route to a destination from a startlocation of the vehicle 301. The system 101 may obtain routinginformation stored in map database 103 a and provide the route fornavigation as part of the requested route to the vehicle 301. Forexample, the route may include road 303 as part of the requested routefor navigation.

The method 600 c further comprises, at step 600 c 3, determining, basedon map data and route information, a plurality of locations associatedwith a confidence value related to an environmental zone in the region.The plurality of locations comprises at least one location falling onthe requested route.

In some embodiments, the requested route may include locations that fallwithin a coverage area of the environmental zone. When the map database301 determines the plurality of locations that make the requested route,then, using the time of day and each location of the plurality oflocations, a confidence value for each location of the plurality oflocations is calculated. For example, using the calculations outlined inFIGS. 4A-4D, and methods 600 a-600 b, confidence value at each locationfor the time of day is calculated. Further, a threshold confidence valuemay be identified. For example, the threshold may be set at 60% or 0.6.Then, high confidence locations with confidence value more than 60% maybe aggregated to form a polygon describing the coverage area of theenvironmental zone. In some embodiments, the clustering may be doneusing any known clustering algorithm, such as DB-SCAN, Affinitypropagation, Gaussian Mixture Model, K-Means, Balanced Iterative ReducedClustering using Hierarchies (BIRCH) and the like, to identify highconfidence locations within the region. Further, using these highconfidence locations, a polygon formation algorithm (e.g. convex hull)may be used to determine the extent of the polygon. The extent of thepolygon then defines the coverage area of the environmental zone in theregion, for the requested route of navigation.

At step 600 c 5, the method 600 c includes, determining the coveragearea for the environmental zone in the region based on the plurality oflocations, and in the form of polygons as described previously. Thepolygons may then be used at step 600 c 7, for providing the routenavigation instructions for the navigation of at least one vehicle, suchas the vehicle 301. The vehicle 301 may be subscribed to the services ofthe mapping platform 103 for receiving navigational alerts. As part ofthese alerts, the polygons defining extent of environmental zone on therequested route of navigation of the vehicle 301 may be sent to thevehicle 301 as it approaches or departs from the environmental zone.

In some embodiments, the polygons defining extent of environmental zoneon the requested route of navigation of the vehicle 301 may be usedduring route planning to avoid the area depicted in the polygon ifactive or to allow the area to be included in the routing if it isestimated to not be active during the expected travel times.

In some embodiments, these navigational alerts may be cancelled using atime-to-live (e.g. 45 minutes) or when the confidence drops below athreshold. The time-to-live parameter can be determined using a sampleof ground truth data.

In some embodiments, the polygon extent may be redetermined. Forexample, when the confidence value associated with at least one locationfor is changed and is determined to fall below the predeterminedthreshold value, the location may be cancelled as falling within thecoverage area of the environmental zone. Further the clusteringalgorithm may be executed again, and the polygon formation algorithm isalso re-executed to determine the new extent of the polygon. The newextent of the polygon defines the updated coverage area of theenvironmental zone.

Thus, the methods 600 a-600 c are configured to provide a continuouslyupdated value of confidence, by monitoring in real time, each of thelocations falling within the region designated for environmental zonedetection. Further, the continuous monitoring also enables provision ofaccurate, real time, up to date and reliable environmental zoneinformation the users.

In some embodiments, the operations described in each of the methods 600a-600 c may enable providing the route navigation instructions fornavigation of the at least one vehicle in the region based on thedetermined coverage area for the environmental zone in the region.

In some embodiments, the route navigation instructions may be related tocontrolling the operation of the at least one vehicle. For example, thevehicle 301 may be alerted that they are approaching a green zone area,so they must switch on an emission control system. Alternately, if thevehicle 301 is an autonomous vehicle, the emission control system may beautomatically switched on to control the emission of pollutants from thevehicle 301.

In some embodiments, the vehicle 301 may be provided alternate routesfor navigation as part of the navigation instructions.

In some embodiments, the vehicle 301 may give a choice of an optimizedroute of travel, a green zone based route of travel or a long route oftravel as part of the navigation instruction.

In some embodiments, the system 101 may provide routing instruction inone or more of a message alert, an audio message or a notification, avisual display, a visual indicator and the like. For example, therouting instruction may comprise displaying on a user interface of theuser equipment 105, an alternate route that is not an environmentalzone. In another embodiment, the routing instruction may instruct thevehicle 301 to change the emission operation so that the vehicle emitsless fuel emission (that is compatible for environmental zone) and stillable to cross the environmental zone.

The methods 600 a-600 c may be implemented using correspondingcircuitry. For example, the method 600 a may be implemented by anapparatus or system comprising a processor, a memory, and acommunication interface of the kind discussed in conjunction with FIG.2.

In some example embodiments, a computer programmable product may beprovided. The computer programmable product may comprise at least onenon-transitory computer-readable storage medium having stored thereoncomputer-executable program code instructions that when executed by acomputer, cause the computer to execute the various methods discussed inFIGS. 6A-6C.

In an example embodiment, an apparatus for performing any of the methods600 a-600 c of FIGS. 6A-6C above may comprise a processor (e.g. theprocessor 201) configured to perform some or each of the operations ofthe methods 600 a-600 c described previously. The processor may, forexample, be configured to perform the operations (600 a 1-600 a 5, 600 b1-600 b 5, and 600 c 1-600 c 7) by performing hardware implementedlogical functions, executing stored instructions, or executingalgorithms for performing each of the operations. Alternatively, theapparatus may comprise means for performing each of the operationsdescribed above. In this regard, according to an example embodiment,examples of means for performing operations (600 a 1-600 a 5, 600 b1-600 b 5, and 600 c 1-600 c 7) may comprise, for example, the processor201 which may be implemented in the system 101 and/or a device orcircuit for executing instructions or executing an algorithm forprocessing information as described above.

In this way, example embodiments of the invention result in detectingthe coverage of environmental zone and generating a time schedule of anenvironmental zone. The generation of the time schedule may help inassisting user to provide alternate route. The invention may help userto alert while driving based on the detection of environmental zone in atimely and targeted way in advance. The invention also updates thecoverage of the environmental zone in a map database.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, although the foregoing descriptions and the associateddrawings describe example embodiments in the context of certain examplecombinations of elements and/or functions, it should be appreciated thatdifferent combinations of elements and/or functions may be provided byalternative embodiments without departing from the scope of the appendedclaims. In this regard, for example, different combinations of elementsand/or functions than those explicitly described above are alsocontemplated as may be set forth in some of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

I/We claim:
 1. A method for detecting an environmental zone, the methodcomprising: obtaining at least one observation associated with theenvironmental zone in a region; determining a confidence valueassociated with the at least one observation in the region; anddetecting the environmental zone in the region based on the confidencevalue associated with the at least one observation in the region,wherein detecting comprises determining either one of a presence or anabsence of the environmental zone in the region.
 2. The method of claim1, wherein detecting the environmental zone further comprises detectinga coverage area associated with the environmental zone in the region. 3.The method of claim 2, further comprising generating a time schedule forthe environmental zone based on the determined coverage area of theenvironmental zone in the region.
 4. The method of claim 3, furthercomprising predicting either one of the presence or absence of theenvironmental zone based on the generated time schedule and the coveragearea of the environmental zone.
 5. The method of claim 1, whereinobtaining the at least one observation associated with the environmentalzone in the region further comprises obtaining the at least oneobservation based on at least one of road sign data, one or morepollution sensors, and one or more other sensors in a vehicle.
 6. Themethod of claim 1, wherein the at least one observation furthercomprises at least one of a positive observation and a negativeobservation, wherein the positive observation is associated with a firstdetermination of presence of environmental zone in the region and thenegative observation is associated with a second determination ofabsence of environmental zone in the region.
 7. The method of claim 6,wherein each of the at least one positive observation and the at leastone negative observation is associated with a time interval associatedwith each day in a week.
 8. The method of claim 6, wherein determiningthe confidence value associated with the at least one observationcomprises: determining a plurality of observations in the region for thetime interval associated with the at least one observation, wherein theplurality of observations include a plurality of positive observationsand a plurality of negative observations; aggregating the plurality ofpositive observations to determine an aggregated positive observationvalue; aggregating the plurality of negative observations to determinean aggregated negative observation value; and determining the confidencevalue based on the aggregated positive observation value and theaggregated negative observation value.
 9. The method of claim 1, whereindetermining the confidence value further comprises monitoring, in realtime, a change in confidence value associated with the at least oneobservation in the region.
 10. The method of claim 1, further comprisingdetermining confidence value of at least one missing observation for theregion based on a historical confidence value associated with the atleast one observation.
 11. The method of claim 1, further comprisinggenerating navigational alerts associated with the detection of theenvironmental zone in the region.
 12. The method of claim 1, furthercomprising generating alternate routes for navigation based on thedetection of the environmental zone.
 13. The method of claim 1, furthercomprising updating a coverage of the environmental zone in a mapdatabase, wherein the coverage is indicated by a polygon shape in themap database.
 14. The method of claim 1, wherein the region comprises atleast one of a location point, a map tile area, a road segment, and alane.
 15. A system for generating a time schedule of an environmentalzone in a region, the system comprising: a memory configured to storecomputer-executable instructions; and one or more processors configuredto execute the instructions to: obtain, for a predefined time interval,at least one observation associated with the environmental zone in theregion; determine, for the predefined time interval, a confidence valueassociated with the at least one observation; and generate a timeschedule for the environmental zone in the region based on thedetermined confidence value and the predefined time interval.
 16. Thesystem of claim 15, wherein the one or more processors are furtherconfigured to: determine for the predefined interval, a plurality ofobservations comprising a plurality of positive observations associatedwith the environmental zone in the region for the predefined timeinterval and a plurality of negative observations associated with theenvironmental zone in the region for the predefined time interval;aggregate, for the region and the predefined time interval, both theplurality of positive observations and the plurality of negativeobservations to determine a corresponding aggregated positiveobservation value and a corresponding aggregated negative observationvalue; determine, for the region and the predefined time interval, theconfidence value based on the aggregated positive observation value andthe aggregated negative observation value; detect, for the region andthe predefined time interval, either one of a presence or an absence ofthe environmental zone based on the confidence value; and generate thetime schedule of the environmental zone based on the detection.
 17. Thesystem of claim 16, wherein to generate the time schedule of theenvironmental zone in the region, the one or more processors are furtherconfigured to: identify a plurality of time intervals for each day in aweek; detect, for each sub-interval in the plurality of time intervals,either one of the presence or absence of the environmental zone in theregion, wherein each sub-interval corresponds to the predefined timeinterval; and generate the time schedule of the environmental zone inthe region based on detection, wherein the generated time schedulecomprises data indicating either one of the presence or absence of theenvironmental zone in the region for each sub-interval in the pluralityof time intervals for each day in the week.
 18. The system of claim 17,wherein the one or more processors are further configured to generatenavigational alerts for at least one vehicle based on the generated timeschedule, wherein the navigation alerts comprise one or more routenavigation instructions for the at least one vehicle based on thepresence or absence of the environmental zone in the region at a time ofnavigation of the at least one vehicle through the region.
 19. Thesystem of claim 15, wherein the one or more processors are furtherconfigured to: identify, in real-time, a change in the confidence valueassociated with the at least one observation; and generate an updatedtime schedule for the environmental zone in the region based on thedetermined change in the confidence value and the predefined timeinterval.
 20. A computer programmable product comprising anon-transitory computer readable medium having stored thereon computerexecutable instructions which when executed by one or more processors,cause the one or more processors to carry out operations for providingnavigation instructions, the operations comprising: obtaining routeinformation for navigation of at least one vehicle in a region;determining, based on map data and the route information, at least onelocation associated with a confidence value related to an environmentalzone in the region; determining a coverage area for the environmentalzone in the region based on the determined at least one location; andproviding the navigation instructions for operation of the at least onevehicle in the region based on the determined coverage area for theenvironmental zone in the region.